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Our Courses
Data Processing and Feature Engineering with MATLAB
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background.
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Course by
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Self Paced
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20 hours
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English
Six Sigma Advanced Define and Measure Phases
This course is for you if you are looking to dive deeper into Six Sigma or strengthen and expand your knowledge of the basic components of green belt level of Six Sigma and Lean. Six Sigma skills are widely sought by employers both nationally and internationally. These skills have been proven to help improve business processes and performance.
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Course by
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Self Paced
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15 hours
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English
Basic Statistics
Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance).
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Course by
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Self Paced
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27 hours
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English
Understanding and Visualizing Data with Python
In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data.
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Course by
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Self Paced
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21 hours
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English
Foundations of Data Science: K-Means Clustering in Python
Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government. This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses.
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Course by
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Self Paced
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29 hours
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English
Bayesian Statistics: Mixture Models
Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application. Some exercises require the use of R, a freely-available statistical software package.
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Course by
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Self Paced
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22 hours
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English
State Estimation and Localization for Self-Driving Cars
Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course. This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car.
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Course by
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Self Paced
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27 hours
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English
Introduction to Statistics & Data Analysis in Public Health
Welcome to Introduction to Statistics & Data Analysis in Public Health! This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series.
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Course by
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Self Paced
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16 hours
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English
Foundations of strategic business analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role.
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Course by
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Self Paced
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8 hours
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English
Strategic Career Self-Management
According to the U.S. Bureau of Labor Statistics, in the current economy, Americans shift jobs every 4-5 years, which translates into 8-10 job transitions, including several career changes, over one’s life time. With competition for jobs on the rise, a person’s continuing employability and career success are increasingly defined by his or her mastery of individual career development and the ability to effectively "manage oneself" (P. Drucker). In this course, you will learn how to think strategically and professionally about your career self-management.
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Course by
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Self Paced
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13 hours
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English
Introduction to Probability and Data with R
This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project.
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Course by
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Self Paced
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14 hours
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English
Applied Machine Learning in Python
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled.
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Course by
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31 hours
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English
Inferential and Predictive Statistics for Business
This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. To this end, the course aims to cover statistical ideas that apply to managers by discussing two basic themes: first, is recognizing and describing variations present in everything around us, and then modeling and making decisions in the presence of these variations. The fundamental concepts studied in this course will reappear in many other classes and business settings.
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Course by
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Self Paced
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19 hours
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English
Linear Regression for Business Statistics
Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. This is the fourth course in the specialization, "Business Statistics and Analysis". The course introduces you to the very important tool known as Linear Regression. You will learn to apply various procedures such as dummy variable regressions, transforming variables, and interaction effects.
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Course by
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Self Paced
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28 hours
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English
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it?
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Course by
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Self Paced
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24 hours
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English
Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions
The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers.
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Course by
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Self Paced
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21 hours
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English
Business Analytics for Decision Making
In this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation. You will then learn the basics of Monte Carlo simulation that will help you model the uncertainty that is prevalent in many business decisions. A key element of decision making is to identify the best course of action. Since businesses problems often have too many alternative solutions, you will learn how optimization can help you identify the best option.
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Course by
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Self Paced
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9 hours
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English
Data Management and Visualization
Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before.
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Course by
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Self Paced
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12 hours
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English
Business Applications of Hypothesis Testing and Confidence Interval Estimation
Confidence intervals and Hypothesis tests are very important tools in the Business Statistics toolbox. A mastery over these topics will help enhance your business decision making and allow you to understand and measure the extent of ‘risk’ or ‘uncertainty’ in various business processes. This is the third course in the specialization "Business Statistics and Analysis" and the course advances your knowledge about Business Statistics by introducing you to Confidence Intervals and Hypothesis Testing. We first conceptually understand these tools and their business application.
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Course by
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Self Paced
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25 hours
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English
Exploring and Producing Data for Business Decision Making
This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. Specifically, you will learn how to summarize data and learn concepts of frequency, normal distribution, statistical studies, sampling, and confidence intervals. While you will be introduced to some of the science of what is being taught, the focus will be on applying the methodologies.
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Course by
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Self Paced
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23 hours
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English
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas.
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Course by
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Self Paced
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13 hours
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English
Investments I: Fundamentals of Performance Evaluation
In this course, we will discuss fundamental principles of trading off risk and return, portfolio optimization, and security pricing. We will study and use risk-return models such as the Capital Asset Pricing Model (CAPM) and multi-factor models to evaluate the performance of various securities and portfolios. Specifically, we will learn how to interpret and estimate regressions that provide us with both a benchmark to use for a security given its risk (determined by its beta), as well as a risk-adjusted measure of the security’s performance (measured by its alpha).
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Course by
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Self Paced
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26 hours
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English
Introduction to Data Analysis Using Excel
The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics later. The course is designed keeping in mind two kinds of learners - those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills.
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Course by
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Self Paced
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24 hours
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English
Measurement Systems Analysis
In this course, you will learn to analyze measurement systems for process stability and capability and why having a stable measurement process is imperative prior to performing any statistical analysis. You will analyze continuous measurement systems and statistically characterize both accuracy and precision using R software. You will perform measurement systems analysis for potential, short-term and long-term statistical control and capability.
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Course by
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17 hours
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English
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars.
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Course by
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Self Paced
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35 hours
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English